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Found 13 papers, 1 papers with code
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications
There is increasing interest in data-driven approaches for recommending optimal treatment strategies in many chronic disease management and critical care applications.
Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling
However, in healthcare where the number of contributing institutions are likely not of a colossal scale, computing exact SVs is still exorbitantly expensive, but not impossible.
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification
Deep learning approaches achieve state-of-the-art performance for classifying radiology images, but rely on large labelled datasets that require resource-intensive annotation by specialists.
Domain-specific Language Pre-training for Dialogue Comprehension on Clinical Inquiry-Answering Conversations
There is growing interest in the automated extraction of relevant information from clinical dialogues.
Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean
Specifically, we describe adaptations for scenarios with 2D and 3D inputs, uni and multi-label classification, and class distribution mismatch between labeled and unlabeled portions of the training data.
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks
While neural approaches have achieved significant improvement in machine comprehension tasks, models often work as a black-box, resulting in lower interpretability, which requires special attention in domains such as healthcare or education.
Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations
In this paper, we propose a self-supervised CNN approach to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images.
Joint Learning of Word and Label Embeddings for Sequence Labelling in Spoken Language Understanding
We propose an architecture to jointly learn word and label embeddings for slot filling in spoken language understanding.
Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring
Data for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data are even scarcer in healthcare.
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks.
Attention-based Semantic Priming for Slot-filling
The problem of sequence labelling in language understanding would benefit from approaches inspired by semantic priming phenomena.
Online Deep Learning: Growing RBM on the fly
The online generative training begins with zero neurons in the hidden layer, adds and updates the neurons to adapt to statistics of streaming data in a single pass unsupervised manner, resulting in a feature representation best suited to the data.
